Showing 1 - 19 results of 19 for search 'International Conference on Learning Representations', query time: 0.25s Refine Results
  1. 1
    Reasoning about physical interactions with object-oriented prediction and planning

    Reasoning about physical interactions with object-oriented prediction and planning by Janner, Michael, Levine, Sergey, Freeman, William T, Tenenbaum, Joshua B, Finn, Chelsea, Wu, Jiajun

    Published 2020
    “…International Conference on Learning Representations…”
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    Article
  2. 2
    Stochastic prediction of multi-agent interactions from partial observations

    Stochastic prediction of multi-agent interactions from partial observations by Sun, Chen, Karlsson, Per, Wu, Jiajun, Tenenbaum, Joshua B, Murphy, Kevin P

    Published 2020
    “…International Conference on Learning Representations…”
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    Article
  3. 3
    The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision

    The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision by Mao, Jiayuan, Gan, Chuang, Kohli, Pushmeet, Tenenbaum, Joshua B, Wu, Jiajun

    Published 2020
    “…International Conference on Learning Representations…”
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    Article
  4. 4
    TreeQN and ATreeC: differentiable tree planning for deep reinforcement learning

    TreeQN and ATreeC: differentiable tree planning for deep reinforcement learning by Farquhar, G, Rocktaeschel, T, Igl, M, Whiteson, S

    Published 2018
    “…International Conference on Learning Representations…”
    Conference item
  5. 5
    How robust is unsupervised representation learning to distribution shift?

    How robust is unsupervised representation learning to distribution shift? by Shi, Y, Daunhawer, I, Vogt, JE, Torr, PHS, Sanyal, A

    Published 2023
    “…International Conference on Learning Representations…”
    Conference item
  6. 6
    A generalization of the randomized singular value decomposition

    A generalization of the randomized singular value decomposition by Boulle, N, Townsend, A

    Published 2021
    “…International Conference on Learning Representations…”
    Conference item
  7. 7
    Deep structured output learning for unconstrained text recognition

    Deep structured output learning for unconstrained text recognition by Jaderberg, M, Simonyan, K, Vedaldi, A, Zisserman, A

    Published 2015
    “…International Conference on Learning Representations…”
    Conference item
  8. 8
    The concrete distribution: A continuous relaxation of discrete random variables

    The concrete distribution: A continuous relaxation of discrete random variables by Maddison, C, Mnih, A, Teh, Y

    Published 2017
    “…International Conference on Learning Representations…”
    Conference item
  9. 9
    VariBAD: a very good method for Bayes-adaptive deep RL via meta-learning

    VariBAD: a very good method for Bayes-adaptive deep RL via meta-learning by Zintgraf, L, Shiarlis, K, Igl, M, Schulze, S, Gal, Y, Hofmann, K, Whiteson, S

    Published 2020
    “…International Conference on Learning Representations…”
    Conference item
  10. 10
    Optimistic exploration even with a pessimistic initialisation

    Optimistic exploration even with a pessimistic initialisation by Whiteson, S, Rashid, T, Peng, B, Bohmer, W

    Published 2020
    “…International Conference on Learning Representations…”
    Conference item
  11. 11
    Stable rank normalization for improved generalization in neural networks and GANs

    Stable rank normalization for improved generalization in neural networks and GANs by Sanyal, A, Torr, P, Dokania, P

    Published 2020
    “…International Conference on Learning Representations…”
    Conference item
  12. 12
    Learning to compose words into sentences with reinforcement learning

    Learning to compose words into sentences with reinforcement learning by Yogatama, D, Blunsom, P, Dyer, C, Grefenstette, E, Ling, W

    Published 2017
    “…International Conference on Learning Representations…”
    Journal article
  13. 13
    A signal propagation perspective for pruning neural networks at initialization

    A signal propagation perspective for pruning neural networks at initialization by Lee, N, Ajanthan, T, Gould, S, Torr, PHS

    Published 2019
    “…International Conference on Learning Representations…”
    Conference item
  14. 14
    Multilingual distributed representations without word alignment

    Multilingual distributed representations without word alignment by Hermann, K, Blunsom, P

    Published 2014
    “…International Conference on Learning Representations…”
    Journal article
  15. 15
    Deep inside convolutional networks: visualising image classification models and saliency maps

    Deep inside convolutional networks: visualising image classification models and saliency maps by Simonyan, K, Vedaldi, A, Zisserman, A

    Published 2014
    “…International Conference on Learning Representations…”
    Conference item
  16. 16
    Information asymmetry in KL-regularized RL

    Information asymmetry in KL-regularized RL by Galashov, A, Jayakumar, SM, Hasenclever, L, Tirumala, D, Schwarz, J, Desjardins, G, Czarnecki, WM, Teh, YW, Pascanu, R, Heess, N

    Published 2018
    “…International Conference on Learning Representations…”
    Journal article
  17. 17
    Learn to pay attention

    Learn to pay attention by Jetley, S, Lord, NA, Lee, N, Torr, PH

    Published 2018
    “…International Conference on Learning Representations…”
    Conference item
  18. 18
    Deep Frank-Wolfe for neural network optimization

    Deep Frank-Wolfe for neural network optimization by Berrada, L, Zisserman, A, Mudigonda, P

    Published 2019
    “…International Conference on Learning Representations…”
    Conference item
  19. 19
    Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

    Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas by Kuo, Yen-Ling, Katz, Boris, Barbu, Andrei

    Published 2022
    “…Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR)…”
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    Article

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